Triple
T169604
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Kobe |
E3089
|
entity |
| Predicate | rankInJapanByPopulation |
P1169
|
FINISHED |
| Object | one of the ten largest cities in Japan |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: one of the ten largest cities in Japan | Statement: [Kobe, rankInJapanByPopulation, one of the ten largest cities in Japan]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: rankInJapanByPopulation Context triple: [Kobe, rankInJapanByPopulation, one of the ten largest cities in Japan]
-
A.
gdpRankInJapan
Indicates the position of an entity in the ordered ranking of GDP values within Japan.
-
B.
hasPopulationRank
Indicates the relative position of an entity in an ordered list based on the size of its population.
-
C.
cityPopulationContext
Indicates the contextual relationship between a city and information about its population, such as size, distribution, or demographic characteristics.
-
D.
continentRankByPopulation
Indicates the relative position of a continent in an ordered list based on its population size.
-
E.
populationRank
chosen
Indicates the relative position of an entity in an ordered list based on the size of its population.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a2524ce1e48190ab066bf72859f474 |
completed | Feb. 28, 2026, 2:26 a.m. |
| NER | Named-entity recognition | batch_69a258b6f4f88190b1264bbbeb19a29e |
completed | Feb. 28, 2026, 2:53 a.m. |
| PD | Predicate disambiguation | batch_69a25665f5b8819096ca3e084faf976e |
completed | Feb. 28, 2026, 2:43 a.m. |
Created at: Feb. 28, 2026, 2:34 a.m.